Book Image

Practical Big Data Analytics

By : Nataraj Dasgupta
Book Image

Practical Big Data Analytics

By: Nataraj Dasgupta

Overview of this book

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulated in this book.
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface

Common terminologies in machine learning


In machine learning, you'll often hear the terms features, predictors, and dependent variables. They are all one and the same. They all refer to the variables that are used to predict an outcome. In our previous example of cars, the variables cyl (Cylinder), hp (Horsepower), wt (Weight), and gear (Gear) are the predictors and mpg (Miles Per Gallon) is the outcome.

In simpler terms, taking the example of a spreadsheet, the names of the columns are, in essence, known as features, predictors, and dependent variables. As an example, if we were given a dataset of toll booth charges and were tasked with predicting the amount charged based on the time of day and other factors, a hypothetical example could be as follows:

In this spreadsheet, the columns date, time, agency, type, prepaid, and rate are the features or predictors, whereas, the column amount is our outcome or dependent variable (what we are predicting).

The value of amount depends on the value of...